Predictive radiosensitivity network model

ABSTRACT

This invention is a model that simulates the complexity of biological signaling in a cell in response to radiation therapy. Using gene expression profiles and radiation survival assays in an algorithm, a systems model was generated of the radiosensitivity network. The network consists of ten highly interconnected genetic hubs with significant signal redundancy. The model was validated with in vitro tests perturbing network components, correctly predicting radiation sensitivity 2/3 times. The model&#39;s clinical relevance was shown by linking clinical radiosensitivity targets to the model network. Clinical applications were confirmed by testing model predictions against clinical response to preoperative radiochemotherapy in patients with rectal or esophageal cancer.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to currently pending U.S. ProvisionalPatent Application 60/896,350, entitled, “Radiation Response SystemModel”, filed Mar. 22, 2007, and pending U.S. Provisional PatentApplication 60/896,550, entitled, “Radiation Response System Model”,filed Mar. 23, 2007 the contents of which are herein incorporated byreference.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with Government support under Grant No. K08 CA108926 awarded by the National Cancer Institute. The Government hascertain rights in the invention.

FIELD OF INVENTION

This invention relates to cancer treatment. Specifically this inventionis a predictive model of cancer radiosensitization.

BACKGROUND AND SUMMARY OF THE INVENTION

Understanding the biological networks that regulate oncogenic events andinfluence the inherent radiosensitivity of tumors is central to thedevelopment of personalized treatment strategies in radiation oncology,including targeted and improved therapeutic interventions. During thelast two decades, many key components and signaling pathways in theoncogenic network have been elucidated by studying radiophenotypicchanges after network components are perturbed. However, the dynamics ofnetwork component interactions have remained mostly undefined, largelydue to lack of accurate testing methods.

The generation of high-throughput datasets in the “omic” era has beencentral to the development of a systems-view of complex biologicalsystems. In systems biology, the goal is to understand the dynamics ofthe system and how components interact during operation (H. Kitano,Computational Systems Biology, Nature 420:6912, 2002, 206-210) (H.Kitano, Systems Biology: A Brief Overview, Science 295:5560, 2002,1662-1664) (L. Hood, J. Heath, Systems Biology and New TechnologiesEnable Predictive and Preventative Medicine, Science, 306:5696, 2004,640-643) (L. Hood, R. Perlmutter, The Impact of Systems Approaches onBiological Problems in Drug Discovery, Nat. Biotech., 22:10, 2004,1215-1217). To study complex biological interactions within a networkmodel, novel methods are needed. A central experimental approach inmolecular biology has focused on studying biological systems aftercomponents are perturbed by activation/inactivation. A problem of thisapproach is that it is unable to capture and study the continuous natureof many phenotypic features in diseased and normal states. Analternative approach is a systems-view of biological networks where thefocus is on understanding the dynamics and structure of the system ofinterest. A common feature of systems biology is the development of drycomputational models which exploit comprehensive datasets ofhigh-throughput measurements. A common denominator in these models isthat biological hypothesis can be generated for testing in “wet”experiments, thus allowing the validation of the models and the dynamicsstudied. Computational models have been key in the development ofcentral concepts in neurobiology.

SUMMARY OF THE INVENTION

Provided is a mathematical model that facilitates the study of radiationresponse by providing a systems view of the radiosensitivity network.The model predictions were tested against several biological andclinical endpoints. The systems-based approach improves the ability todefine network dynamics and structure, allows the visualization ofnetwork topology, and provides a framework to understand its operation,thus leading to a better understanding of the variables that driveradiation sensitivity. Furthermore, as the model accounts for networkinteractions, the model to captures the variability of radiationresponse across biological/clinical conditions. This allows thepossibility of developing an accurate predictive model of clinicalradiosensitivity, a major clinical goal in radiation oncology.

A multivariate linear regression model of gene expression andradiosensitivity (SF2) was developed in a 48 cell line dataset withinthe context of an accurate radiation sensitivity classifier. Aliterature review of 35 cell lines in the dataset identified theradiation sensitivity of the cells using SF2 data. Radiation sensitivityfor the remaining cell lines was established by establishing the genomicexpression of each cell line. After a baseline gene expression datasetwas obtained, the cells were irradiated with 2Gy and difference in geneexpression determined using microarrays, which allowed for selection ofexpressed genes based on the gene's statistical correlation between theexpression of the gene of interest and the radiation sensitivity of thecell line expressing the gene

The genetic information for the cell line dataset was analyzed using thedeveloped regression model, thereby identifying at least one gene ofinterest, which is predictive of a radiation response. The regressionmodel identified 500 genes reactive to radiation induction. The 500genes were analyzed using GeneGo to map interconnections between thegenes, and identified a network of interacting genes. This data wasfurther restricted by selecting genes with at least 5 connections toother genes and no more than 50% of the edges hidden within the network.

A series of dynamic cellular states were defined by incorporatingbiological interactions that has been shown to perturb radiosensitivity.The biological interactions of these common radiation response elementswere defined by gene models using the best linear fit model andanalyzing the variability of radiation response in multiple cell linesto identify the significant response elements.

The model was then applied to each gene in an expanded genomic/SF2database of 48 cell lines. This design of an in silico model includes adiverse group of cancer cell lines and favors the identification ofgenes that are important across cell lines and are more likely centralcomponents of the radiation signaling network. The mathematical modelalso allows the development of biological predictions that can beconfirmed by in vitro experiments. This allows feedback into whether theinterpretations of the mathematics represented in the model are ofbiological value. Although the presence of multiple cell lines accountedfor component variability, we sought to integrate actual dynamic statesof the network. The hypothetical dynamic states were defined byincorporating into the linear regression model biological variables thathave been reported to perturb the radiation response network: TO, rasstatus (mut/wt) and p53 status along with gene expression.

The resulting predictive algorithm identified five components offunctional/biological relevance to the network that proved best atbuilding the most accurate predictor, genes rbap48, topi, rgs19, r5piaand an unknown gene. rbap48 and rgs-19 were biologically-validated asnetwork components. siRNA knockdown of rbap48 resulted inradioresistance in HCT-116 cell lines, while overexpression of rgs-19led to radiosensitization of MDA-MB23 1 cell lines, both observationswere consistent with model predictions. In contrast, overexpression ofr5pia resulted in no radiophenotypic change of MALME-3M melanoma cells.Finally, top-1 is a clinically validated radiosensitizing target incurrent clinical use. Thus, we conclude that the linear regression modelis reasonably accurate at identifying radiosensitivity networkcomponents.

The invention allows for predicting a clinical response to radiationtherapy of a patient. Samples of the target cells were collected fromthe patients. The genomic expression of the collected sample wasdetermined by microarray analysis and the data applied to the networkmodel. High expression values correlated with a radiosensitive phenotypeand predicted the clinical response to treatment with radiation therapy.In a specific embodiment, the target cells comprise cancerous cells.

A mathematical model has been developed to represent the topology of theradiation response network. The model identifies novel components of theradiation network as well as integrates dynamics and variability intobiological predictions. Both of these abilities have been biologicallyvalidated. The model is also envisioned useful in biomarker discovery,allowing biomarkers of response or of radiophenotype to be identifiedusing the model. The model is also useful for clinical trial designs.Network architecture proposed by the model has resulted in identifiednodes, which allow for drug designs to specifically target those nodes.This is also useful to guide clinicians in proposing novel combinationsof known drugs in clinical trials. Additionally the model may provide anapproach to dissect the complexity of network operation. For example, amodel detailing the contribution of each hub in the network to finalsystem output can be derived from our database. Finally, a similar modelis useful in identifying chemotherapy response using cellular/genomicdatabases.

BRIEF DESCRIPTION OF THE DRAWINGS

For a fuller understanding of the invention, reference should be made tothe following detailed description, taken in connection with theaccompanying drawings, in which:

FIG. 1 is an illustration showing a flowchart of the multivariate linearregression model classifier algorithm.

FIG. 2 is a graph showing the radiation response illustration showingthe linear regression model output using RbAp48. The output predictsknockout of RbAp48 will result in radioresistance.

FIG. 3 shows the experimental outcome of knocking out RbAp48 in HCT116cells. (A) Cell survival rates are graphed based on radiation exposure,and (B) a western blot of RbAp48 protein after siRNA transfection.

FIG. 4 is a graph showing the radiation response illustration showingthe linear regression model output using RGS-19. The output predictsknockout of RGS-19 will result in radiosensitivity.

FIG. 5 shows the experimental outcome of knocking out RGS-19 in MDA231cells. (A) Cell survival rates are graphed based on radiation exposure,and (B) a western blot of RGS-19 protein after siRNA transfection.

FIG. 6 is a graph showing the radiation response illustration showingthe linear regression model output using Ribose 5 Phosphate Isomerase A(R5PIA). The output predicts overexpression of R5PIA will result inradiosensitivity.

FIG. 7 shows the experimental outcome of knocking out R5PIA in SKMEL28cells. (A) Cell survival rates are graphed based on total radiationexposure, and (B) a western blot of RGS-19 protein after siRNAtransfection.

FIG. 8 is a table of the significant pathways defined by GeneGO MetaCoreanalysis for the top 500 genes identified by linear fit.

FIG. 9 is a table of selected pathways from the GeneGO MetaCore analysisof significant terms using ANOVA.

FIG. 10 is a table of significant pathways found in Dynamic State 2.

FIG. 11 is a table of significant pathways found in Dynamic State 3.

FIG. 12 is a table of significant pathways found in Dynamic State 4.

FIG. 13 is an illustration showing the network model of the radiationresponse. The topology of major hubs is shown.

FIG. 14 is a table showing the gene distribution of the data probesetagainst the dynamic states.

FIG. 15 is a table showing the radiation network hub genes. Genes ingray were used as central hubs for the classifier. The probesets used oneach platform are listed for each hub.

FIG. 16 is a table showing the network model predictions for threecancer types.

FIG. 17 is a table of experimental data for three cancer types. The datavalidates the model predictions, seen in FIG. 16.

FIG. 18 is a graph showing leave-one-out cross-validation results forhub-based classifier on dataset cell lines.

FIG. 19 is a table showing leave-one-out cross-validation predictions onthe dataset cell lines using a rank-based linear classifier on theproposed radiation network hubs.

FIG. 20 shows topotecan radiation sensitivity predictions and resultsfor rectal cancer patients. (A) A table of rectal cancer samples showsthe radiation sensitivity using survival fractions and clinicalresponse, and (B) a graph of predicted outcomes of rectal cancerradiotherapy, as defined by the network model.

FIG. 21 shows radiation sensitivity predictions and results foresophageal cancer patients. (A) A table of rectal cancer samples showsthe radiation sensitivity using survival fractions and clinicalresponse, and (B) a graph of predicted outcomes of esophageal cancerradiotherapy, as defined by the network model.

FIG. 22 is a graph showing a summary of predicted responses for bothrectal and esophageal cancer radiosensitivity. As seen, the model notedsignificant radiation sensitivity response between responders andnon-responders.

FIG. 23 is a graph showing the summary of experimental data from rectaland esophageal cancer radiosensitivity.

FIG. 24 is a table showing the manner in which radiosensitizationtargets are linked to the radiation sensitivity network.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A multivariate linear regression model of gene expression andradiosensitivity (SF2) was developed in a 35 cell line database withinthe context of an accurate radiation sensitivity classifier. Theclinical value of a radiosensitivity predictive model is significant,therefore an understanding the intricacies of its operation werecritical. The predictive algorithm identified five components offunctional/biological relevance to the network that proved best atbuilding the most accurate predictor, genes rbap48, top1, rgs19, r5piaand an unknown gene. FIG. 1 shows a schematic representation of theclassifier algorithm. As shown in FIGS. 2-5, rbap48 and rgs-19 werebiologically-validated as network components. Consistent with modelpredictions, depicted in FIG. 2, siRNA knockdown of rbap48 in HCT-116cells, seen in FIG. 3( b), resulted in radioresistance as seen in FIG.3( a). Next, G-protein signaling regulator rgs-19 was overexpressed inMDA-MB231 cell lines, seen in FIG. 5( b). Overexpression led to enhancedradiosensitization of the cell lines compared to EV-transfectedcontrols, as seen in FIG. 5( a). This was consistent with modelpredictions, seen in FIG. 4. In contrast, overexpression of r5pia inMALME-3M melanoma cell lines, seen in FIG. 7( b), resulted in noradiophenotypic change, as seen in FIG. 7( a). This result was contraryto the predicted response, seen in FIG. 6. Finally, top-1 is aclinically validated radiosensitizing target in current clinical use,and confirmed that the linear regression model is reasonably accurate atidentifying radiosensitivity network components.

The model was more fully developed to map the radiation sensitivitynetwork by incorporating biological interactions with the genomic/SF2database. A linear model was created for each gene in the cell linedataset. Common elements of radiation response were analyzed forvariability introduced by multiple cell lines in the classifier toexplicitly model the dynamic states. The dynamic states were modelsincorporating biological variables that have been reported to perturbthe radiosensitivity network: tissue of origin (TO), ras status (mut/wt)and p53 status along with gene expression. The resulting model:

SF2=k ₀ +k ₁(y _(x))+k ₂(TO)+k ₃(ras status)+k ₄(p53 status)+k ₅(y_(x))(TO)+k ₆(y _(x))(ras status)+k ₇(TO)(ras status)+k ₈(y _(x))(p ₅₃status)+k ₉(TO)(p53)+k ₁₀(ras status)(p53 status)+k ₁₁(y _(x))(TO)(rasstatus)+k ₁₂(y _(x))(ras status)(p53 status)+k ₁₃(TO)(rasstatus)(p53status)+k ₁₄(y _(x))(TO)(ras status)(p53 status)

represents 14 different potential dynamic states based on the fourchosen variables and interactions between those variables. Original cellline data was created on HU6800 GeneChips while the newer patient datawas created on HG-U133Plus Chips. The probesets were translated using ablast program to identify the best U133Plus probeset match to theconsensus sequence from which the 6800 probeset was designed usingAffymetrix software. The 500 genes identified with the smallest sum ofsquared residuals for the developed linear model were further analyzedusing Analysis of Variance (ANOVA) to determine the significant terms(e.g. gene, ras status) and correlation to SF2 across cell lines. Themodel produced four significant dynamic states in the radiosensitivitynetwork, reduced from the 14 hypothetical states. TO and ras status andtheir interaction with gene expression proved to be key variables indefining the four states. Interestingly, the prostate cancer TO termgrouped separately. In contrast p53 was not found to be a significantfactor in the analysis. Cell lines grouped in the three states mainlydistinguished by the presence of a mutated ras. The ras wt populationwas divided in two groups (NSCLC and Ovarian vs. Other TO). The ras termwas dominant, therefore cell lines with mutated ras grouped closer thancell lines from the same TO, as exemplified by breast cancer cell lines(HS578T, MDAMB231) grouped together with other cell lines that sharedthis biological feature.

To explore the functional difference in the dynamic states, a pathwayanalysis was performed. GeneGO MetaCore identified a series ofsignificant pathways shared by the 500 genes, depicted in FIG. 8. Asseen in FIG. 9, key biological differences exist across dynamic statesin the network. For example, dynamic state 2 represented pathways inmetabolism, hypoxia and Akt, seen in FIG. 10. Dynamic state 3represented 29 pathways, 11 of which were cell cycle related, seen inFIG. 11. Finally, dynamic state 4 was the most functionally diverse,representing pathways in DNA repair, cell cycle regulation, adhesion,apoptosis, immune response and protein kinase cascades, seen in FIG. 12.While many of these pathways have been implicated in the regulation ofradiation response, the model evidences the importance of each pathway'sdependence on the biological context that defines the dynamics of thenetwork.

To visualize the network proposed by the mathematical model, the primaryinterconnections of the original 500 genes selected usingliterature-based annotations were plotted using GeneGO, seen in FIG. 13.The gene probesets were loaded into GeneGO MetaCore and analyzed foroverexpression in various cellular pathways defined by the dynamicstates, seen in FIG. 14. Hubs were defined within the gene network as anode consisting of at least 5 connections to other genes, seen in FIG.15. All hubs with more than 5 connections and less than 50% of edgeshidden within the network were chosen as the major hubs forclassification purposes. This network model, shown in FIG. 13, proposesten central hubs: c-jun, HDAC-1, RelA (p65 subunit of NFKB), PKC,SUMO-1, c-Abl, STAT-1, AR, CDK1 and IRF1, seen in FIG. 15. Remarkably,each of these hubs is reportedly involved in radiation signaling and6/10 (HDAC1, NF-KB, c-ABL, STAT1, AR, SUMO-1) have been proposed astargets for radiosensitizer development. Additionally, the modelproposes significant cross-talk among the central hubs, consistent witha robust system with significant signal redundancy. It should be notedthat these hubs would not be identified using the correlation of geneexpression to SF2 as the median R of these hubs is 0.02.

Because the hub classifier was applied to datasets generated fromdiffering GeneChip platform and technology, genes were normalized usinga rank-based approach. Gene expression was ranked for each gene persample using the identified ten hubs.

The model was analyzed by testing the effect of c-jun knockdown onradiosensitivity, thereby determining whether biologically-relevantnetwork dynamics and interactions were being captured. Selection ofc-jun was due to the fact that c-jun is a central hub and an AP-1regulated pathway was the only commonality between the three maindynamic states. Importantly, c-jun has been shown to play a role as anearly response gene in the initial stages of radiation response. Themodel predicted c-jun knockdown would cause differing results, based onthe biological context as defined by TO. The predictions andexperimental outcomes, using a linear fit for c-jun gene expression toSF2 stratified by TO, are shown in FIG. 16. As seen in FIG. 17, c-junsiRNA was transfected into 8 different cell lines, representing thethree tissues types selected: NSCLC, colon and breast. Downregulation ofc-jun resulted in induction of radiation resistance in NSCLC cell lines,consistent with the linear regression curve derived from the model. Cellsurvival curves in both A549 and H460 cell lines confirmed theseobservations. Furthermore, the radiophenotype of colon cancer cell lines(when considered as a group) was unaffected by c-jun downregulation,also supporting the model. However, it should be noted that in HCT-116cells, c-jun downregulation led to radioresistance (p=0.52).Additionally, radiation response in breast cancer cell lines wasunchanged by c-jun siRNA transfection, while the linear model predictedradiosensitization. The model was experimentally validated in two ofthree tested instances, for lung cancer (radioresistance, p=0.005) andcolon cancer (no change).

The experiments supported the model's ability to capture the influenceof biological context on network outcome. However, becauseradiosensitivity prediction is linked to biological contexts,predicitive features changed depending on expression context. Ahub-based gene expression classifier was built to estimate thepredictive accuracy of the network model. A linear regression model wasdeveloped along with support-vector machines for comparison, however,the linear regression model found the most accurate at 30/48 (62.5%),seen in FIG. 18. Further, the rank-based dataset normalization yielded amore accurate classifier than using actual gene expression values, shownin FIG. 19.

To determine the clinical relevance of the model, it was used to predictclinical response in 14 patients with locally-advanced rectal cancertreated with preoperative concurrent radiochemotherapy. Pre-treatmentsamples from the patients were arrayed on the HG-U133Plus platform. Thetumors were staged at initial biopsy with ultrasound and later stagesusing pathological information from surgical resection. Downstaging inthe T stage from the TNM staging system translated R (response) in thedataset, while no change or progressive disease was recorded as NR (noresponse). Data was processed using gcRMA using the Bioconductorimplementation. Gene expression values for the 10 hubs were converted toranks and SF2 values were generated from the model created using cellline data, depicted in FIG. 20 (mean predicted SF2 R vs. NR 0.31 vs0.45, p=0.03). Responders were further tested for significantly lowerpredicted SF2 using a one sided Wilcoxon rank-sum test (P=0.02964). The10 gene model was further tested in a cohort of 12 patients withesophageal cancer also treated with preoperative radiochemotherapy. Apre-treatment biopsy was collected from the patients and tissue arrayedon the HG-U133Plus platform. The entire dataset was processed (22patient samples), though only 12 esophageal cancer samples withchemoradiation response were available. Chips were normalized using RMAin the GENE implementation (Eschrich, 2007). Gene expression values forthe ten hubs were converted to rank values and the SF2 values weregenerated from the model created using cell line data. Similar to therectal cancer cohort, responders were predicted to be moreradiosensitive than non-responders as determined by predicted SF2, seenin FIG. 21 (0.34 vs. 0.48, p=0.05). For both patient cohorts, rectal andesophageal cancer, the model predictions significantly separatedpathological responders (R) from non-responders (NR), seen in FIGS. 22and 23. A test of significantly lower predicted SF2 values in the CRgroup was performed using a one-sided Wilcoxon rank-sum test(P=0.05303). These results are encouraging since no esophageal cancercell lines were included in the original database, suggesting that themodel is capturing central common aspects of the radiosensitivitynetwork that are of clinical relevance.

The model was further analyzed against ten known radiosensitizer drugtargets, both in clinical development or routine clinical use. All drugtargets are linked by primary interconnection to at least one centralhub of the model, seen in FIG. 24, supporting the clinical relevance ofthe radiosensitity network model. Moreover, the model revealed that thetargets interference with only a minority of the hubs, suggesting thecurrent clinical approach to radiosensitization is inefficient atdisrupting the radiosensitivity network.

A fundamental objective of the field of systems biology is to develop anunderstanding of the dynamics and structure of complex biologicalsystems. The presented model integrates both of these elements andrepresents an important advance in the understanding of the radiationresponse regulatory network.

The mathematical model proposes a highly interconnected network topologywith ten central hubs and significant signal redundancy. The redundancyexplains why targeting a single hub could lead to different orinconsistent system outputs (i.e c-jun knockout), as phenotypicresponses may be driven by competing signal networks. The complexcombination of signals is consistent with the continuous nature ofradiation response, providing a framework to explain individual responsevariability. The hubs identified by the model have been shown importantin the regulation of radioresponse. All targets connected via at leastone of hub, supporting the biological validity of the model. Incontrast, 20 alternative networks were developed using chance forfeature selection. The mathematical model outperformed all alternativechance networks in all instances, when target connectivity and hub'srelevance in radioresponse were used as benchmarks for comparison.

An advantage of the mathematical model is that it considers the inherentindividual variability that exists in the response to therapeuticagents. Furthermore, biological variables that may define specificresistance/sensitivity phenotypes can be included, allowing the model tocapture several signaling states in the network. This last concept hasbeen proposed to explain the lack of commonality between validateddisease-specific molecular signatures in clinical oncology. The modelcan identify novel network components and integrate complex interactionsand dynamics into biological predictions. Finally, it provides a networkarchitecture that allows hypothesis development, extending from basicradiation molecular biology to hypothesis with a direct impact inclinical radiation oncology.

Material and Methods

Cell lines—Cell lines were obtained directly from the National CancerInstitute (NCI). Cells were cultured in RPMI 1640 media supplementedwith glutamine (2 mM), antibiotics (penicillin/streptomycin, 10 U/ml)and Heat inactivated Fetal Bovine Serum (10%) at 37° C. with anatmosphere of 5% CO2.

Radiation Survival Assays (SF2)—The SF2 of cell lines used in theclassifier were obtained from the literature in 23 of the 48 cell linesin our analysis. For cell lines obtained from the literature, papers(published before 2004) were used that reported on clonogenic assaysthat had been performed without the use of any substrate (i.e. agar) andthat required cells to be in log phase at the time of irradiation. Thecell lines also needed at least two reported values in the literature bydifferent laboratories. Mean SF2 values were determined for each cellline and used for the generation of the model. The remaining 25 celllines (MCF-7, MDA-MB-435, KM-12, HOP62, H23, BT549, MDA-MB-231, HCT116,HT29, H460, OVCAR5 and PC3) SF2 values were determined in the lab.Clonogenic survival assays after 2 Gy of radiation were performed aspreviously described (J. Staunton, D. Slonim, ChemosensitivityPrediction by Transcriptional Profiling, Proc. Nat. Acad. Sci., 98:19,10787-10792). Plating efficiency for each cell line was determined,prior to SF2 determination. Cells were plated so that 50-100 colonieswould form per plate and incubated overnight at 37° C. overnight toallow for adherence. Cells were then radiated with 2 Gy using a CesiumIrradiator (J. L Sheperd, Model I 68A, San Fernando, Calif.). Exposuretime was adjusted for decay every three months. After irradiation cellswere incubated for 10-14 days at 37° C. before being stained withcrystal violet. Only colonies with at least 50 cells were counted. SF2was determined by the following formula:

SF2=number of colonies/total number of cells plated×plating efficiency.

Microarrays—Gene expression profiles were from Affymetrix HU6800 chips(7,129 genes) or from a previously published study (J. Torres-Roca, S.Eschrich, et al., Prediction of Radiation Sensitivity Using a GeneExpression Classifier, Cancer Res., 65:16, 7169-7176). The geneexpression data had been previously preprocessed using the AffymetrixMAS 4.0 algorithm in average difference units. Negative expressionvalues were set to zero and the chips were normalized to the same meanintensity.

siRNA transfection. 3×10⁵ Hs-RbAp48-hi cells in 2 mL antibiotic-freecomplete medium were plated in each well of a six-well plate and after24 h of incubation were transfected following the basic dharmaFECTtransfection protocol (Dharmacon, Inc., Lafayette, Colo.) with either apool of 4 negative control siRNAs (siRNA pool) or RbAp48 siRNA designedby Dharmacon's SMARTpool technology both at 100 nM final concentration.48 hours after transfection, cells were lysed for Western blotting, toconfirm the knockdown of RbAp48, or plated in coverslips forimmunofluorescence.

The disclosure of all publications cited above are expresslyincorporated herein by reference, each in its entirety, to the sameextent as if each were incorporated by reference individually.

It will be seen that the advantages set forth above, and those madeapparent from the foregoing description, are efficiently attained andsince certain changes may be made in the above construction withoutdeparting from the scope of the invention, it is intended that allmatters contained in the foregoing description or shown in theaccompanying drawings shall be interpreted as illustrative and not in alimiting sense.

It is also to be understood that the following claims are intended tocover all of the generic and specific features of the invention hereindescribed, and all statements of the scope of the invention which, as amatter of language, might be said to fall therebetween. Now that theinvention has been described,

1. A method of generating a radiation network model for predictingcellular radiation sensitivity comprising the steps of: developing amultivariate linear regression model of radiosensitivity and geneexpression comprising: establishing the radiation sensitivity of atleast one cell line; establishing the genomic expression of the at leastone cell line; selecting expressed genes based on statistical relevanceto radiation induction; and identifying at least one gene of interest,expressed by the at least one cell line, predictive of a radiationresponse using the regression model; identifying radiosensitivitynetwork components using the multivariate linear regression model,comprising: identifying genes reactive to radiation induction using themultivariate linear regression model; and identifying interconnectedgenes; and incorporating biological interactions of common radiationresponse elements with the radiosensitivity network components.
 2. Themethod of claim 1, wherein the radiation sensitivity of the cell line isestablished by the survival fraction of the cell line after exposure toabout 2 Gy of radiation.
 3. The method of claim 1, wherein genomicexpression of the at least one cell line is established from amicroarray.
 4. The method of claim 3, wherein the microarray data istranslated using a blast program to match consensus sequences of geneprobesets.
 5. The method of claim 1, wherein the radiosensitivitynetwork components are identified by: plotting the interconnection ofgene reactive to radiation induction; and selecting genes with at least5 connections to other genes.
 6. The method of claim 1, wherein thecommon radiation response elements are major hubs of the radiationnetwork model comprising: genes with at least 5 connections to othergenes; and no more than 50% of the edges hidden within the network. 7.The method of claim 4, wherein the radiosensitivity network componentsare selected from the group consisting of: c-jun, HDAC-1, RelA, PKC,SUMO-1, c-Abl, STAT-1, AR, CDK1, and IRF1.
 8. The method of claim 1,wherein the selected expressed genes are identified by measuring thecorrelation between the expression of the gene of interest and theradiation sensitivity of the cell line expressing the gene.
 9. Themethod of claim 1, wherein the biological interactions of commonradiation response elements are incorporated by: creating a linear modelfor each gene in the dataset; analyzing the variability of radiationresponse in multiple cell lines.
 10. The method of claim 1, wherein thecommon radiation response elements are selected from the groupconsisting of: tissue origin, ras mutation status, p53 status, tissueorigin interaction with gene expression, ras mutation status interactionwith gene expression, and p53 status interaction with gene expression.11. The method of claim 7, wherein the genomic data is normalized byranking each gene using the radiosensitivity network components.
 12. Amethod of predicting a clinical response to anticancer therapy of apatient in need thereof comprising: obtaining a sample of target cellsfrom the patient; establishing the genomic expression of the sample; andapplying the genomic expression of the sample to a multivariate linearregression model of treatment sensitivity whereby a high expressionvalue correlates with a treatment sensitive phenotype thereby predictingthe clinical response to treatment.
 13. The method of claim 12, whereinthe anticancer therapy is selected from the group consisting ofradiation therapy, radiochemotherapy, and chemotherapy.
 14. The methodof claim 12, wherein the sample of target cells comprises cancer cells.15. The method of claim 12, wherein the multivariate linear regressionmodel is created comprising the steps of: developing a multivariatelinear regression model of radiosensitivity and gene expressioncomprising: establishing the radiation sensitivity of at least one cellline; establishing the genomic expression of the at least one cell line;selecting expressed genes based on statistical relevance to radiationinduction; and identifying at least one gene of interest, expressed bythe at least one cell line, predictive of a radiation response using aregression model; identifying radiosensitivity network components usingthe multivariate linear regression model, comprising: identifying genesreactive to radiation induction using the multivariate linear regressionmodel; and identifying interconnected genes; and incorporatingbiological interactions of common radiation response elements with theradiosensitivity network components.
 16. The method of claim 15, whereingenomic expression of the cell line is established from a microarray.17. The method of claim 15, wherein the radiosensitivity networkcomponents are identified by: plotting the interconnection of genereactive to radiation induction; and selecting genes with at least 5connections to other genes.
 18. The method of claim 17, wherein theradiosensitivity network components are selected from the groupconsisting of: c-jun, HDAC-1, RelA, PKC, SUMO-1, c-Abl, STAT-1, AR,CDK1, and IRF1.
 19. The method of claim 15, wherein the selectedexpressed genes are identified by measuring the correlation between theexpression of the gene of interest and the radiation sensitivity of thecell line expressing the gene.
 20. The method of claim 15, wherein thecommon radiation response elements are selected from the groupconsisting of: tissue origin, ras mutation status, p53 status, tissueorigin interaction with gene expression, ras mutation status interactionwith gene expression, and p53 status interaction with gene expression.